Overview

Dataset statistics

Number of variables17
Number of observations167
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.3 KiB
Average record size in memory136.8 B

Variable types

Categorical3
Numeric14

Warnings

area_name has a high cardinality: 167 distinct values High cardinality
area_code has a high cardinality: 167 distinct values High cardinality
score_2007 is highly correlated with score_2008 and 12 other fieldsHigh correlation
score_2008 is highly correlated with score_2007 and 12 other fieldsHigh correlation
score_2009 is highly correlated with score_2007 and 12 other fieldsHigh correlation
score_2010 is highly correlated with score_2007 and 12 other fieldsHigh correlation
score_2011 is highly correlated with score_2007 and 12 other fieldsHigh correlation
score_2012 is highly correlated with score_2007 and 12 other fieldsHigh correlation
score_2013 is highly correlated with score_2007 and 12 other fieldsHigh correlation
score_2014 is highly correlated with score_2007 and 12 other fieldsHigh correlation
score_2015 is highly correlated with score_2007 and 12 other fieldsHigh correlation
score_2016 is highly correlated with score_2007 and 12 other fieldsHigh correlation
score_2017 is highly correlated with score_2007 and 12 other fieldsHigh correlation
score_2018 is highly correlated with score_2007 and 12 other fieldsHigh correlation
score_2019 is highly correlated with score_2007 and 12 other fieldsHigh correlation
score_2020 is highly correlated with score_2007 and 12 other fieldsHigh correlation
area_name is uniformly distributed Uniform
area_code is uniformly distributed Uniform
area_name has unique values Unique
area_code has unique values Unique

Reproduction

Analysis started2021-04-21 21:39:25.973766
Analysis finished2021-04-21 21:39:50.335010
Duration24.36 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

area_name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct167
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Benin
 
1
Netherlands
 
1
Malaysia
 
1
Denmark
 
1
Oman
 
1
Other values (162)
162 

Length

Max length28
Median length7
Mean length8.263473054
Min length4

Characters and Unicode

Total characters1380
Distinct characters58
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique167 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAngola
5th rowArgentina
ValueCountFrequency (%)
Benin1
 
0.6%
Netherlands1
 
0.6%
Malaysia1
 
0.6%
Denmark1
 
0.6%
Oman1
 
0.6%
Guinea-Bissau1
 
0.6%
Bangladesh1
 
0.6%
Poland1
 
0.6%
Belize1
 
0.6%
Albania1
 
0.6%
Other values (157)157
94.0%
2021-04-22T04:39:50.868052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and3
 
1.5%
united3
 
1.5%
republic3
 
1.5%
guinea3
 
1.5%
south3
 
1.5%
congo2
 
1.0%
new2
 
1.0%
sudan2
 
1.0%
china2
 
1.0%
mauritius1
 
0.5%
Other values (179)179
88.2%

Most occurring characters

ValueCountFrequency (%)
a214
15.5%
i123
 
8.9%
n108
 
7.8%
e95
 
6.9%
r77
 
5.6%
o76
 
5.5%
u52
 
3.8%
t49
 
3.6%
l47
 
3.4%
d39
 
2.8%
Other values (48)500
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1141
82.7%
Uppercase Letter200
 
14.5%
Space Separator36
 
2.6%
Other Punctuation2
 
0.1%
Dash Punctuation1
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
a214
18.8%
i123
10.8%
n108
9.5%
e95
 
8.3%
r77
 
6.7%
o76
 
6.7%
u52
 
4.6%
t49
 
4.3%
l47
 
4.1%
d39
 
3.4%
Other values (20)261
22.9%
ValueCountFrequency (%)
S23
 
11.5%
C19
 
9.5%
M15
 
7.5%
B14
 
7.0%
A13
 
6.5%
G12
 
6.0%
T12
 
6.0%
L10
 
5.0%
N10
 
5.0%
I9
 
4.5%
Other values (14)63
31.5%
ValueCountFrequency (%)
'1
50.0%
,1
50.0%
ValueCountFrequency (%)
36
100.0%
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1341
97.2%
Common39
 
2.8%

Most frequent character per script

ValueCountFrequency (%)
a214
16.0%
i123
 
9.2%
n108
 
8.1%
e95
 
7.1%
r77
 
5.7%
o76
 
5.7%
u52
 
3.9%
t49
 
3.7%
l47
 
3.5%
d39
 
2.9%
Other values (44)461
34.4%
ValueCountFrequency (%)
36
92.3%
'1
 
2.6%
-1
 
2.6%
,1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1376
99.7%
None4
 
0.3%

Most frequent character per block

ValueCountFrequency (%)
a214
15.6%
i123
 
8.9%
n108
 
7.8%
e95
 
6.9%
r77
 
5.6%
o76
 
5.5%
u52
 
3.8%
t49
 
3.6%
l47
 
3.4%
d39
 
2.8%
Other values (44)496
36.0%
ValueCountFrequency (%)
ô1
25.0%
ã1
25.0%
é1
25.0%
í1
25.0%

area_code
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct167
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
SAU
 
1
ROU
 
1
BWA
 
1
TTO
 
1
CAN
 
1
Other values (162)
162 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters501
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique167 ?
Unique (%)100.0%

Sample

1st rowAFG
2nd rowALB
3rd rowDZA
4th rowAGO
5th rowARG
ValueCountFrequency (%)
SAU1
 
0.6%
ROU1
 
0.6%
BWA1
 
0.6%
TTO1
 
0.6%
CAN1
 
0.6%
GNQ1
 
0.6%
THA1
 
0.6%
AFG1
 
0.6%
KWT1
 
0.6%
MKD1
 
0.6%
Other values (157)157
94.0%
2021-04-22T04:39:51.163623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mus1
 
0.6%
mli1
 
0.6%
lbr1
 
0.6%
fra1
 
0.6%
bel1
 
0.6%
blr1
 
0.6%
srb1
 
0.6%
uzb1
 
0.6%
png1
 
0.6%
kwt1
 
0.6%
Other values (157)157
94.0%

Most occurring characters

ValueCountFrequency (%)
A38
 
7.6%
R38
 
7.6%
N36
 
7.2%
M31
 
6.2%
S28
 
5.6%
G27
 
5.4%
L27
 
5.4%
B25
 
5.0%
E25
 
5.0%
T24
 
4.8%
Other values (16)202
40.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter501
100.0%

Most frequent character per category

ValueCountFrequency (%)
A38
 
7.6%
R38
 
7.6%
N36
 
7.2%
M31
 
6.2%
S28
 
5.6%
G27
 
5.4%
L27
 
5.4%
B25
 
5.0%
E25
 
5.0%
T24
 
4.8%
Other values (16)202
40.3%

Most occurring scripts

ValueCountFrequency (%)
Latin501
100.0%

Most frequent character per script

ValueCountFrequency (%)
A38
 
7.6%
R38
 
7.6%
N36
 
7.2%
M31
 
6.2%
S28
 
5.6%
G27
 
5.4%
L27
 
5.4%
B25
 
5.0%
E25
 
5.0%
T24
 
4.8%
Other values (16)202
40.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII501
100.0%

Most frequent character per block

ValueCountFrequency (%)
A38
 
7.6%
R38
 
7.6%
N36
 
7.2%
M31
 
6.2%
S28
 
5.6%
G27
 
5.4%
L27
 
5.4%
B25
 
5.0%
E25
 
5.0%
T24
 
4.8%
Other values (16)202
40.3%

area_group
Categorical

Distinct7
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Sub-Saharan Africa
49 
Asia-Pacific
29 
Latin America and the Caribbean
25 
Eastern Europe
23 
Western Europe
20 
Other values (2)
21 

Length

Max length31
Median length18
Mean length18.95209581
Min length12

Characters and Unicode

Total characters3165
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsia-Pacific
2nd rowEastern Europe
3rd rowMiddle East and North Africa
4th rowSub-Saharan Africa
5th rowLatin America and the Caribbean
ValueCountFrequency (%)
Sub-Saharan Africa49
29.3%
Asia-Pacific29
17.4%
Latin America and the Caribbean25
15.0%
Eastern Europe23
13.8%
Western Europe20
12.0%
Middle East and North Africa19
 
11.4%
North America2
 
1.2%
2021-04-22T04:39:51.414509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-22T04:39:51.499566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
africa68
15.6%
sub-saharan49
11.2%
and44
10.1%
europe43
9.8%
asia-pacific29
 
6.6%
america27
 
6.2%
latin25
 
5.7%
the25
 
5.7%
caribbean25
 
5.7%
eastern23
 
5.3%
Other values (4)79
18.1%

Most occurring characters

ValueCountFrequency (%)
a461
14.6%
r276
 
8.7%
270
 
8.5%
i251
 
7.9%
e202
 
6.4%
n186
 
5.9%
c153
 
4.8%
t133
 
4.2%
A124
 
3.9%
b99
 
3.1%
Other values (18)1010
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2371
74.9%
Uppercase Letter446
 
14.1%
Space Separator270
 
8.5%
Dash Punctuation78
 
2.5%

Most frequent character per category

ValueCountFrequency (%)
a461
19.4%
r276
11.6%
i251
10.6%
e202
8.5%
n186
7.8%
c153
 
6.5%
t133
 
5.6%
b99
 
4.2%
f97
 
4.1%
h95
 
4.0%
Other values (7)418
17.6%
ValueCountFrequency (%)
A124
27.8%
S98
22.0%
E85
19.1%
P29
 
6.5%
L25
 
5.6%
C25
 
5.6%
N21
 
4.7%
W20
 
4.5%
M19
 
4.3%
ValueCountFrequency (%)
-78
100.0%
ValueCountFrequency (%)
270
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2817
89.0%
Common348
 
11.0%

Most frequent character per script

ValueCountFrequency (%)
a461
16.4%
r276
 
9.8%
i251
 
8.9%
e202
 
7.2%
n186
 
6.6%
c153
 
5.4%
t133
 
4.7%
A124
 
4.4%
b99
 
3.5%
S98
 
3.5%
Other values (16)834
29.6%
ValueCountFrequency (%)
270
77.6%
-78
 
22.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3165
100.0%

Most frequent character per block

ValueCountFrequency (%)
a461
14.6%
r276
 
8.7%
270
 
8.5%
i251
 
7.9%
e202
 
6.4%
n186
 
5.9%
c153
 
4.8%
t133
 
4.2%
A124
 
3.9%
b99
 
3.1%
Other values (18)1010
31.9%

score_2007
Real number (ℝ≥0)

HIGH CORRELATION

Distinct140
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.18622754
Minimum30.7
Maximum83.7
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2021-04-22T04:39:51.635523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum30.7
5-th percentile35.91
Q144.75
median53.9
Q363.9
95-th percentile79.94
Maximum83.7
Range53
Interquartile range (IQR)19.15

Descriptive statistics

Standard deviation13.54127522
Coefficient of variation (CV)0.2453741779
Kurtosis-0.7111211428
Mean55.18622754
Median Absolute Deviation (MAD)9.8
Skewness0.4156581768
Sum9216.1
Variance183.3661345
MonotocityNot monotonic
2021-04-22T04:39:51.780736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
563
 
1.8%
40.73
 
1.8%
46.53
 
1.8%
44.83
 
1.8%
63.72
 
1.2%
45.22
 
1.2%
72.62
 
1.2%
52.82
 
1.2%
81.92
 
1.2%
48.82
 
1.2%
Other values (130)143
85.6%
ValueCountFrequency (%)
30.71
0.6%
32.81
0.6%
331
0.6%
33.11
0.6%
33.71
0.6%
ValueCountFrequency (%)
83.71
0.6%
831
0.6%
82.31
0.6%
81.92
1.2%
80.91
0.6%

score_2008
Real number (ℝ≥0)

HIGH CORRELATION

Distinct139
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.43173653
Minimum29.5
Maximum83.8
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2021-04-22T04:39:51.926116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum29.5
5-th percentile36.32
Q145.1
median54
Q364.2
95-th percentile80.15
Maximum83.8
Range54.3
Interquartile range (IQR)19.1

Descriptive statistics

Standard deviation13.55585096
Coefficient of variation (CV)0.244550357
Kurtosis-0.702385745
Mean55.43173653
Median Absolute Deviation (MAD)9.5
Skewness0.4007919154
Sum9257.1
Variance183.7610952
MonotocityNot monotonic
2021-04-22T04:39:52.074346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.83
 
1.8%
43.83
 
1.8%
58.33
 
1.8%
392
 
1.2%
61.92
 
1.2%
78.82
 
1.2%
56.32
 
1.2%
46.82
 
1.2%
45.92
 
1.2%
37.92
 
1.2%
Other values (129)144
86.2%
ValueCountFrequency (%)
29.51
0.6%
32.61
0.6%
32.81
0.6%
331
0.6%
341
0.6%
ValueCountFrequency (%)
83.81
0.6%
83.11
0.6%
82.51
0.6%
82.21
0.6%
82.11
0.6%

score_2009
Real number (ℝ≥0)

HIGH CORRELATION

Distinct143
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.51676647
Minimum31.1
Maximum83.6
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2021-04-22T04:39:52.208419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum31.1
5-th percentile36.56
Q145.35
median54.1
Q364.35
95-th percentile79.87
Maximum83.6
Range52.5
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.50353659
Coefficient of variation (CV)0.243233485
Kurtosis-0.7134696366
Mean55.51676647
Median Absolute Deviation (MAD)9.3
Skewness0.3945699054
Sum9271.3
Variance182.3455003
MonotocityNot monotonic
2021-04-22T04:39:52.351153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.34
 
2.4%
51.83
 
1.8%
73.32
 
1.2%
47.92
 
1.2%
56.22
 
1.2%
79.32
 
1.2%
46.32
 
1.2%
54.82
 
1.2%
38.72
 
1.2%
56.12
 
1.2%
Other values (133)144
86.2%
ValueCountFrequency (%)
31.11
0.6%
32.31
0.6%
32.91
0.6%
331
0.6%
34.11
0.6%
ValueCountFrequency (%)
83.61
0.6%
83.21
0.6%
82.51
0.6%
82.11
0.6%
821
0.6%

score_2010
Real number (ℝ≥0)

HIGH CORRELATION

Distinct137
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.49820359
Minimum28.9
Maximum83.2
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2021-04-22T04:39:52.490314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum28.9
5-th percentile36.04
Q145.5
median54.1
Q364.2
95-th percentile79.52
Maximum83.2
Range54.3
Interquartile range (IQR)18.7

Descriptive statistics

Standard deviation13.44459166
Coefficient of variation (CV)0.242252736
Kurtosis-0.7112648635
Mean55.49820359
Median Absolute Deviation (MAD)9.6
Skewness0.3554981725
Sum9268.2
Variance180.7570449
MonotocityNot monotonic
2021-04-22T04:39:52.635540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.83
 
1.8%
39.33
 
1.8%
443
 
1.8%
463
 
1.8%
52.72
 
1.2%
63.72
 
1.2%
53.72
 
1.2%
56.32
 
1.2%
72.82
 
1.2%
47.12
 
1.2%
Other values (127)143
85.6%
ValueCountFrequency (%)
28.91
0.6%
31.71
0.6%
32.31
0.6%
331
0.6%
33.41
0.6%
ValueCountFrequency (%)
83.22
1.2%
82.11
0.6%
81.82
1.2%
80.81
0.6%
80.41
0.6%

score_2011
Real number (ℝ≥0)

HIGH CORRELATION

Distinct150
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.66526946
Minimum29.8
Maximum83.5
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2021-04-22T04:39:52.773057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum29.8
5-th percentile37.18
Q145.55
median54.3
Q364.3
95-th percentile79.68
Maximum83.5
Range53.7
Interquartile range (IQR)18.75

Descriptive statistics

Standard deviation13.33586038
Coefficient of variation (CV)0.2395723673
Kurtosis-0.6996152685
Mean55.66526946
Median Absolute Deviation (MAD)9.7
Skewness0.3521579423
Sum9296.1
Variance177.8451721
MonotocityNot monotonic
2021-04-22T04:39:52.913023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.23
 
1.8%
553
 
1.8%
342
 
1.2%
42.42
 
1.2%
40.32
 
1.2%
47.72
 
1.2%
56.32
 
1.2%
64.32
 
1.2%
52.92
 
1.2%
51.92
 
1.2%
Other values (140)145
86.8%
ValueCountFrequency (%)
29.81
0.6%
321
0.6%
32.11
0.6%
33.11
0.6%
33.31
0.6%
ValueCountFrequency (%)
83.51
0.6%
82.61
0.6%
82.21
0.6%
81.91
0.6%
81.51
0.6%

score_2012
Real number (ℝ≥0)

HIGH CORRELATION

Distinct133
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.89161677
Minimum31.7
Maximum83.5
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2021-04-22T04:39:53.044344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum31.7
5-th percentile36.86
Q145.5
median54.7
Q364.7
95-th percentile79.85
Maximum83.5
Range51.8
Interquartile range (IQR)19.2

Descriptive statistics

Standard deviation13.28855569
Coefficient of variation (CV)0.2377557935
Kurtosis-0.6909065292
Mean55.89161677
Median Absolute Deviation (MAD)9.7
Skewness0.3393686015
Sum9333.9
Variance176.5857124
MonotocityNot monotonic
2021-04-22T04:39:53.181878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.63
 
1.8%
75.83
 
1.8%
50.43
 
1.8%
42.32
 
1.2%
41.32
 
1.2%
48.62
 
1.2%
54.12
 
1.2%
46.82
 
1.2%
54.52
 
1.2%
56.22
 
1.2%
Other values (123)144
86.2%
ValueCountFrequency (%)
31.71
0.6%
31.92
1.2%
32.71
0.6%
33.42
1.2%
33.51
0.6%
ValueCountFrequency (%)
83.51
0.6%
83.21
0.6%
821
0.6%
81.91
0.6%
81.71
0.6%

score_2013
Real number (ℝ≥0)

HIGH CORRELATION

Distinct139
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.98862275
Minimum31.7
Maximum83.4
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2021-04-22T04:39:53.315113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum31.7
5-th percentile37.02
Q146.05
median54.7
Q364.75
95-th percentile79.57
Maximum83.4
Range51.7
Interquartile range (IQR)18.7

Descriptive statistics

Standard deviation13.25302611
Coefficient of variation (CV)0.2367092716
Kurtosis-0.6978108145
Mean55.98862275
Median Absolute Deviation (MAD)9.5
Skewness0.3393700448
Sum9350.1
Variance175.6427011
MonotocityNot monotonic
2021-04-22T04:39:53.457479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
563
 
1.8%
783
 
1.8%
39.93
 
1.8%
49.92
 
1.2%
43.32
 
1.2%
47.42
 
1.2%
45.22
 
1.2%
54.72
 
1.2%
56.82
 
1.2%
48.32
 
1.2%
Other values (129)144
86.2%
ValueCountFrequency (%)
31.71
0.6%
32.11
0.6%
32.52
1.2%
33.61
0.6%
33.81
0.6%
ValueCountFrequency (%)
83.41
0.6%
82.91
0.6%
82.41
0.6%
82.21
0.6%
821
0.6%

score_2014
Real number (ℝ≥0)

HIGH CORRELATION

Distinct140
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.24251497
Minimum30.7
Maximum82.6
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2021-04-22T04:39:53.593176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum30.7
5-th percentile37.06
Q146
median55.3
Q365.65
95-th percentile79.74
Maximum82.6
Range51.9
Interquartile range (IQR)19.65

Descriptive statistics

Standard deviation13.23766262
Coefficient of variation (CV)0.2353675441
Kurtosis-0.7077656764
Mean56.24251497
Median Absolute Deviation (MAD)10
Skewness0.292169664
Sum9392.5
Variance175.2357117
MonotocityNot monotonic
2021-04-22T04:39:53.729257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.23
 
1.8%
54.43
 
1.8%
54.93
 
1.8%
57.13
 
1.8%
45.32
 
1.2%
43.32
 
1.2%
55.72
 
1.2%
46.22
 
1.2%
56.22
 
1.2%
45.12
 
1.2%
Other values (130)143
85.6%
ValueCountFrequency (%)
30.71
0.6%
30.81
0.6%
32.41
0.6%
33.21
0.6%
33.51
0.6%
ValueCountFrequency (%)
82.61
0.6%
82.51
0.6%
82.41
0.6%
82.31
0.6%
821
0.6%

score_2015
Real number (ℝ≥0)

HIGH CORRELATION

Distinct143
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.42035928
Minimum29.5
Maximum82.7
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2021-04-22T04:39:53.860464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum29.5
5-th percentile36.58
Q145.8
median55.5
Q365.75
95-th percentile80.05
Maximum82.7
Range53.2
Interquartile range (IQR)19.95

Descriptive statistics

Standard deviation13.3480721
Coefficient of variation (CV)0.2365825434
Kurtosis-0.7081403824
Mean56.42035928
Median Absolute Deviation (MAD)10
Skewness0.2736678137
Sum9422.2
Variance178.1710288
MonotocityNot monotonic
2021-04-22T04:39:53.997354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.43
 
1.8%
48.73
 
1.8%
78.83
 
1.8%
57.23
 
1.8%
59.23
 
1.8%
43.23
 
1.8%
45.82
 
1.2%
52.62
 
1.2%
67.92
 
1.2%
56.62
 
1.2%
Other values (133)141
84.4%
ValueCountFrequency (%)
29.51
0.6%
30.71
0.6%
32.51
0.6%
33.71
0.6%
33.91
0.6%
ValueCountFrequency (%)
82.71
 
0.6%
82.51
 
0.6%
82.43
1.8%
81.71
 
0.6%
81.41
 
0.6%

score_2016
Real number (ℝ≥0)

HIGH CORRELATION

Distinct138
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.39041916
Minimum29.3
Maximum83.1
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2021-04-22T04:39:54.129014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum29.3
5-th percentile36.22
Q145.85
median55.6
Q365.5
95-th percentile80.41
Maximum83.1
Range53.8
Interquartile range (IQR)19.65

Descriptive statistics

Standard deviation13.44478326
Coefficient of variation (CV)0.2384231835
Kurtosis-0.6915382719
Mean56.39041916
Median Absolute Deviation (MAD)9.8
Skewness0.281074167
Sum9417.2
Variance180.7621968
MonotocityNot monotonic
2021-04-22T04:39:54.267105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.83
 
1.8%
34.13
 
1.8%
58.43
 
1.8%
502
 
1.2%
38.12
 
1.2%
58.12
 
1.2%
56.22
 
1.2%
44.92
 
1.2%
60.52
 
1.2%
52.12
 
1.2%
Other values (128)144
86.2%
ValueCountFrequency (%)
29.31
0.6%
30.61
0.6%
32.41
0.6%
33.61
0.6%
33.71
0.6%
ValueCountFrequency (%)
83.11
0.6%
82.91
0.6%
82.72
1.2%
821
0.6%
81.82
1.2%

score_2017
Real number (ℝ≥0)

HIGH CORRELATION

Distinct134
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.59640719
Minimum27.9
Maximum83.9
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2021-04-22T04:39:54.406684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum27.9
5-th percentile35.92
Q145.95
median56.3
Q365.4
95-th percentile80.54
Maximum83.9
Range56
Interquartile range (IQR)19.45

Descriptive statistics

Standard deviation13.55513015
Coefficient of variation (CV)0.239505135
Kurtosis-0.6661245294
Mean56.59640719
Median Absolute Deviation (MAD)10
Skewness0.2532535485
Sum9451.6
Variance183.7415533
MonotocityNot monotonic
2021-04-22T04:39:54.529715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.73
 
1.8%
44.73
 
1.8%
55.13
 
1.8%
57.43
 
1.8%
60.63
 
1.8%
53.63
 
1.8%
43.52
 
1.2%
57.12
 
1.2%
45.92
 
1.2%
41.12
 
1.2%
Other values (124)141
84.4%
ValueCountFrequency (%)
27.91
0.6%
29.81
0.6%
31.81
0.6%
32.71
0.6%
33.71
0.6%
ValueCountFrequency (%)
83.91
0.6%
83.51
0.6%
831
0.6%
82.71
0.6%
82.31
0.6%

score_2018
Real number (ℝ≥0)

HIGH CORRELATION

Distinct137
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.03293413
Minimum28.4
Maximum84.5
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2021-04-22T04:39:54.660395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum28.4
5-th percentile35.92
Q146.25
median56.2
Q365.95
95-th percentile80.8
Maximum84.5
Range56.1
Interquartile range (IQR)19.7

Descriptive statistics

Standard deviation13.65866794
Coefficient of variation (CV)0.2394873796
Kurtosis-0.674509483
Mean57.03293413
Median Absolute Deviation (MAD)10
Skewness0.2360381869
Sum9524.5
Variance186.55921
MonotocityNot monotonic
2021-04-22T04:39:54.790454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.13
 
1.8%
56.73
 
1.8%
69.53
 
1.8%
382
 
1.2%
58.72
 
1.2%
65.72
 
1.2%
60.92
 
1.2%
79.72
 
1.2%
41.72
 
1.2%
60.52
 
1.2%
Other values (127)144
86.2%
ValueCountFrequency (%)
28.41
0.6%
30.41
0.6%
31.21
0.6%
32.11
0.6%
33.21
0.6%
ValueCountFrequency (%)
84.51
0.6%
83.91
0.6%
83.71
0.6%
83.41
0.6%
83.31
0.6%

score_2019
Real number (ℝ≥0)

HIGH CORRELATION

Distinct141
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.2748503
Minimum27.8
Maximum84.1
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2021-04-22T04:39:54.921092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum27.8
5-th percentile36.29
Q146.55
median56.7
Q366.5
95-th percentile80.77
Maximum84.1
Range56.3
Interquartile range (IQR)19.95

Descriptive statistics

Standard deviation13.56533818
Coefficient of variation (CV)0.2368463314
Kurtosis-0.666313335
Mean57.2748503
Median Absolute Deviation (MAD)10
Skewness0.2030008698
Sum9564.9
Variance184.0183998
MonotocityNot monotonic
2021-04-22T04:39:55.045443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.23
 
1.8%
41.42
 
1.2%
60.22
 
1.2%
57.22
 
1.2%
34.12
 
1.2%
54.32
 
1.2%
66.22
 
1.2%
54.52
 
1.2%
632
 
1.2%
56.42
 
1.2%
Other values (131)146
87.4%
ValueCountFrequency (%)
27.81
0.6%
30.61
0.6%
31.11
0.6%
32.21
0.6%
33.61
0.6%
ValueCountFrequency (%)
84.11
0.6%
841
0.6%
83.71
0.6%
83.21
0.6%
831
0.6%

score_2020
Real number (ℝ≥0)

HIGH CORRELATION

Distinct139
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.50898204
Minimum27.9
Maximum84.4
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2021-04-22T04:39:55.178138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum27.9
5-th percentile36.75
Q146.85
median56.9
Q366.85
95-th percentile80.82
Maximum84.4
Range56.5
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.44976296
Coefficient of variation (CV)0.233872388
Kurtosis-0.6949369513
Mean57.50898204
Median Absolute Deviation (MAD)10.1
Skewness0.1911371751
Sum9604
Variance180.8961237
MonotocityNot monotonic
2021-04-22T04:39:55.302440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.93
 
1.8%
57.13
 
1.8%
443
 
1.8%
45.73
 
1.8%
432
 
1.2%
80.22
 
1.2%
58.22
 
1.2%
56.92
 
1.2%
51.62
 
1.2%
41.92
 
1.2%
Other values (129)143
85.6%
ValueCountFrequency (%)
27.91
0.6%
31.41
0.6%
31.91
0.6%
32.51
0.6%
34.11
0.6%
ValueCountFrequency (%)
84.41
0.6%
83.81
0.6%
83.31
0.6%
83.12
1.2%
821
0.6%

Interactions

2021-04-22T04:39:30.178291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:30.290997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:30.397859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:30.504030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:30.607682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:30.709380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:30.816193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:30.922174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:31.037893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:31.254268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:31.360254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:31.460005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:31.562824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:31.684112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:31.815463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:31.932288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:32.037071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:32.149403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:32.258349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:32.362529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:32.465589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:32.567258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:32.669937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:32.776489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:32.878846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:32.988437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:33.101298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:33.205575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:33.309538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:33.417374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:33.523536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:33.625314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:33.731527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:33.845754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:33.955520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:34.060405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:34.165871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:34.267428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:34.367327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:34.467047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:34.566880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:34.792587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:34.905745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:35.027593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:35.144691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:35.261637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:35.372964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:35.487670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:35.599239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:35.708206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:35.818759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:35.925456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:36.028591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:36.135406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:36.246538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:36.350766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:36.452774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:36.566416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:36.669867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:36.771135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:36.870056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:36.968707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:37.068032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:37.171407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:37.274451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:37.382936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:37.483636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:37.583574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:37.682397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:37.783800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:37.882484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:37.982320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:38.083492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:38.188025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:38.292488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:38.397145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:38.497385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:38.611264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:38.726013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:38.826503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:38.949694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:39.201389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:39.301195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:39.404233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:39.506019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:39.606937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:39.707456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:39.805230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:39.902443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:39.998906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:40.094732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:40.191155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:40.287693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:40.391371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:40.504414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:40.612895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:40.711707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:40.808777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:40.905696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:41.002465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:41.102266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:41.208063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:41.313424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:41.414134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:41.511205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:41.607524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:41.715296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:41.821848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:41.926668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:42.023859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:42.122921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:42.230644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:42.337377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:42.436507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:42.531882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:42.627646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:42.724167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:42.834215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:42.947527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:43.053839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:43.156854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:43.259471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:43.357465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:43.454180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:43.553170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:43.650666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:43.754458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:43.858794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:43.961833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:44.057938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:44.342887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:44.445947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:44.546822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:44.651881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:44.754076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:44.855295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:44.955214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:45.058508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:45.159458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:45.257539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:45.354987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:45.460321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:45.565223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:45.665751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:45.769306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:45.871032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:45.972404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:46.075184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:46.181570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:46.280555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:46.405302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:46.504189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:46.609352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:46.709304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:46.808380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:46.915225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:47.017204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:47.117839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:47.217845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:47.318536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:47.423819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:47.534378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:47.632454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:47.731797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:47.836268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:47.958841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:48.068334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:48.173513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:48.274203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:48.388115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:48.496464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:48.595629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:48.694510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:48.791100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:48.888088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:48.989151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:49.087744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:49.185371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:49.282256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:49.381359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:49.483049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T04:39:49.580245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-04-22T04:39:55.767787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-22T04:39:56.007199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-22T04:39:56.209546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-22T04:39:56.419881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-04-22T04:39:49.822434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-22T04:39:50.200880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

area_namearea_codearea_groupscore_2007score_2008score_2009score_2010score_2011score_2012score_2013score_2014score_2015score_2016score_2017score_2018score_2019score_2020
0AfghanistanAFGAsia-Pacific33.734.034.234.234.033.534.835.533.934.134.733.233.634.4
1AlbaniaALBEastern Europe54.955.155.356.357.157.357.257.157.457.757.959.260.159.6
2AlgeriaDZAMiddle East and North Africa48.848.849.349.350.248.948.449.749.950.049.950.951.951.9
3AngolaAGOSub-Saharan Africa35.736.637.138.038.038.637.838.537.737.537.638.038.538.8
4ArgentinaARGLatin America and the Caribbean58.157.958.058.658.458.758.558.357.758.158.760.260.860.5
5ArmeniaARMEastern Europe56.057.456.655.355.155.656.557.157.156.756.958.460.261.4
6AustraliaAUSAsia-Pacific78.878.878.878.778.778.978.578.478.078.678.478.778.678.6
7AustriaAUTWestern Europe79.379.479.679.179.379.579.679.879.579.979.679.780.280.4
8AzerbaijanAZEEastern Europe51.051.251.852.451.852.252.753.253.753.253.354.455.857.7
9BahrainBHRMiddle East and North Africa61.461.961.961.862.160.959.460.060.359.860.660.960.561.3

Last rows

area_namearea_codearea_groupscore_2007score_2008score_2009score_2010score_2011score_2012score_2013score_2014score_2015score_2016score_2017score_2018score_2019score_2020
157United Arab EmiratesAREMiddle East and North Africa63.263.563.463.664.264.264.765.966.166.566.667.466.967.1
158United KingdomGBRWestern Europe78.578.978.878.578.679.579.579.679.680.280.680.880.780.1
159United StatesUSANorth America77.577.778.177.577.176.977.077.277.477.377.277.577.977.5
160UruguayURYLatin America and the Caribbean64.164.864.966.565.966.667.467.467.868.267.768.267.968.2
161UzbekistanUZBAsia-Pacific47.648.648.949.049.150.149.950.851.452.052.152.953.754.4
162VenezuelaVENLatin America and the Caribbean48.849.047.647.147.747.647.945.945.644.343.543.542.542.1
163VietnamVNMAsia-Pacific52.653.053.153.053.253.953.454.054.155.655.656.157.158.3
164YemenYEMMiddle East and North Africa36.436.236.535.835.234.134.134.334.632.431.831.230.631.9
165ZambiaZMBSub-Saharan Africa47.346.847.947.848.148.648.348.448.748.348.448.548.647.5
166ZimbabweZWESub-Saharan Africa38.038.137.236.638.340.340.842.242.141.842.041.742.943.0